Enhancing cost-effectiveness in marine recreational fishing assessment: Flexible model-based estimation of participation rates and effort

IF 3.7 2区社会学 Q2 ENVIRONMENTAL STUDIES

Marine Policy Pub Date : 2025-09-13 DOI:10.1016/j.marpol.2025.106901

Fabio Cevenini , Alice Bartolini , Carlo Fezzi , Silvia Ferrini , Roberta Raffaelli , Martina Scanu , Luca Bolognini , Sasa Raicevich , Fabio Grati

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引用次数: 0

Abstract

Marine Recreational Fisheries (MRF) is a widespread leisure activity with significant ecological and economic implications in coastal regions worldwide. Obtaining accurate estimates of its size is hindered by the dispersed spatial and temporal scales at which recreational fishers operate. Conventional survey-based methods require significant time and financial resources, limiting their application. We provide a new approach for estimating the size of MRF, using a Generalized Additive Model (GAM) hurdle specification, which offers advantages over traditional methods, allowing both estimation in unsampled areas and a more refined spatial resolution. Leveraging a dataset comprising over 16,000 observations from a telephone survey representative of Italy, we find that in our application the GAM provides a better fit than parametric alternatives. Our analysis estimates that 1.5 million individuals engage in MRF in Italy, constituting approximately 2.6 % of the national population, generating 34 million annual fishing trips. Simulation results suggest that our proposed approach remains informative even with smaller sample sizes. We show that a model-based approach could alleviate constraints in MRF estimation, particularly where financial resources are limited. This represents a step forward for incorporating MRF into policies aimed at supporting the health of marine ecosystems.

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提高海洋休闲捕鱼评估的成本效益：基于灵活模型的参与率和工作量估算

海洋休闲渔业（MRF）是一种在世界沿海地区广泛存在的具有重要生态和经济意义的休闲活动。获得其大小的准确估计受到分散的空间和时间尺度的阻碍，在休闲渔民操作。传统的基于调查的方法需要大量的时间和财政资源，限制了它们的应用。我们提供了一种估算MRF大小的新方法，使用广义加性模型（GAM）障碍规范，它比传统方法具有优势，既可以在未采样区域进行估计，又可以获得更精细的空间分辨率。利用来自意大利电话调查代表的超过16,000个观察数据集，我们发现在我们的应用程序中，GAM比参数替代方案提供了更好的拟合。我们的分析估计，意大利有150万人从事MRF，约占全国人口的2.6% %，每年产生3400万次钓鱼。模拟结果表明，即使在较小的样本量下，我们提出的方法仍然具有信息性。我们表明，基于模型的方法可以减轻MRF估计的约束，特别是在财政资源有限的情况下。这代表着将MRF纳入旨在支持海洋生态系统健康的政策方面向前迈进了一步。

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来源期刊

Marine Policy Multiple-

CiteScore

7.60

自引率

13.20%

发文量

428

期刊介绍： Marine Policy is the leading journal of ocean policy studies. It offers researchers, analysts and policy makers a unique combination of analyses in the principal social science disciplines relevant to the formulation of marine policy. Major articles are contributed by specialists in marine affairs, including marine economists and marine resource managers, political scientists, marine scientists, international lawyers, geographers and anthropologists. Drawing on their expertise and research, the journal covers: international, regional and national marine policies; institutional arrangements for the management and regulation of marine activities, including fisheries and shipping; conflict resolution; marine pollution and environment; conservation and use of marine resources. Regular features of Marine Policy include research reports, conference reports and reports on current developments to keep readers up-to-date with the latest developments and research in ocean affairs.